Sistem dapatan semula imej untuk aplikasi perubatan

Dapatan semula imej (DSI) adalah sistem pencarian imej yang menggunakan ciri-ciri tertentu atau konteks khusus dalam sesuatu imej. Dalam bidang perubatan, sistem DSI digunakan untuk menyediakan imej yang diperlukan secara tepat dan pantas kepada pakar perubatan. Proses itu biasanya berlaku pada dan ketika diagnosis dan rawatan penyakit dilakukan. Sistem dapatan semula yang awal dan masih digunakan dengan meluas dalam bidang perubatan adalah sistem DSI berdasarkan teks (TBIRS). TBIRS menggunakan kata kunci dalam konteks sesuatu imej dan ia memerlukan anotasi teks secara manual. Proses anotasi teks adalah tugas yang memerihkan lebih-lebih lagi jika melibatkan pangkalan data yang besar. Ini memungkinkan kebarangkalian berlakunya kesilapan manusia adalah tinggi. Untuk mengatasi masalah yang dinyatakan, sistem DSI berdasarkan kandungan (CBIRS) dengan pengindeksan automatik adalah dicadangkan. Kaedah ini melibatkan pemprosesan imej perubatan berdasarkan komputer yang menggunakan fitur visual imej seperti warna, bentuk dan tesktur. Namun begitu, umum mengetahui bahawa suatu algoritma tertentu dalam CBIRS adalah khusus untuk satu modaliti sahaja dan melibatkan bahagian yang tertentu. Ini ditambahkan pula bahawa CBIRS telah mengabaikan persepsi manusia dalam tugas menakrif sesuatu imej dan akibatnya, menyebabkan wujudnya masalah jurang semantik. Oleh itu, sistem DSI hibrid (HBIRS) yang menggabungkan kekuatan kedua-dua TBIRS dan CBIRS telah diperkenalkan bagi menangani masalah jurang semantik khususnya dan sekaligus memantapkan sistem DSI amnya. Satu kerangka sistem DSI yang cekap iaitu HBIRS juga telah dicadangkan. Walau bagaimanapun, kajian ini hanya melibatkan TBIRS dan CBIRS bagi aplikasi perubatan, dan prototaip TBIRS yang dikaji menggunakan imej X-Ray turut dicadangkan

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

Deep Learning in Chest Radiography: From Report Labeling to Image Classification

Chest X-ray (CXR) is the most common examination performed by a radiologist. Through CXR, radiologists must correctly and immediately diagnose a patient’s thorax to avoid the progression of life-threatening diseases. Not only are certified radiologists hard to find but also stress, fatigue, and lack of experience all contribute to the quality of an examination. As a result, providing a technique to aid radiologists in reading CXRs and a tool to help bridge the gap for communities without adequate access to radiological services would yield a huge advantage for patients and patient care. This thesis considers one essential task, CXR image classification, with Deep Learning (DL) technologies from the following three aspects: understanding the intersection of CXR interpretation and DL; extracting multiple image labels from radiology reports to facilitate the training of DL classifiers; and developing CXR classifiers using DL. First, we explain the core concepts and categorize the existing data and literature for researchers entering this field for ease of reference. Using CXRs and DL for medical image diagnosis is a relatively recent field of study because large, publicly available CXR datasets have not been around for very long. Second, we contribute to labeling large datasets with multi-label image annotations extracted from CXR reports. We describe the development of a DL-based report labeler named CXRlabeler, focusing on inductive sequential transfer learning. Lastly, we explain the design of three novel Convolutional Neural Network (CNN) classifiers, i.e., MultiViewModel, Xclassifier, and CovidXrayNet, for binary image classification, multi-label image classification, and multi-class image classification, respectively. This dissertation showcases significant progress in the field of automated CXR interpretation using DL; all source code used is publicly available. It provides methods and insights that can be applied to other medical image interpretation tasks

Denture - induced fibrous hyperplasia (epulis fissuratum)

Denture-induced fibrous hyperplasia (epulis fissuratum) occurs in complete denture patients, because of constant irritative action that induces the mucosa to grow under poorly fitting dentures. The epulis fissuratm usually occurs in the vestibular mucosa, where the denture flange contacts the tissue. It consists of painless folds of fibrous connective tissue that are firm to palpation. These lesions must be removed, and to avoid a relapse, new complete dentures should be made to maintain healthy surgical tissues. Aim: The purpose of this study was to present a case report of the surgical treatment of epulis fissuratum, as a support to clinical diagnosis with histopathological finding, and to provide satisfactory results of rehabilitation in oral function and tissue health with new denture. An epulis fissuratum is a benign condition but, if ulcerated, it can mimic more serious conditions like oral cancer. Thus, microscopic histopathological examination of the removed tissue is an imperative to be accomplished in order to confirm the doctor's clinical diagnosis

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

A Review of Oropharyngeal Injuries and Case Studies of Soft Tissue Surgical Cases in the Dog

A series of 41 dogs with oropharyngeal injury cases referred to Glasgow University Veterinary Hospital between the period of 1979-1993 were studied. The common cause of the trauma appeared to be pieces of wood in 28 cases (68.2%), and other causes included metallic foreign body (3 cases), bone (2 cases), and one ball. In seven dogs (17%) the cause was not ascertained. The Collie type of breed showed a higher presentation followed by Crossbred and Springer spaniel. Although, not significant statistically, male dogs were over presented (61%). Young (60.9%) and medium to large size dogs (64.8) were the typical victims. The majority of the dogs (84.2%) were chronically presented. The common presenting feature recorded was swelling (20 cases) and most swellings were on the cervical region (1 leases). History of trauma was the main recorded historical finding (43.5%). The typical clinical findings were swelling (29 cases) and discharging sinus (28 cases). The sites of original injury found were sublingual (6 cases), lateral pharyngeal (4 cases), tonsilar- (3 cases), rostral pharyngeal (1 case), and one dorsal pharyngeal. Surgical exploration was performed in 38 of the cases. The outcome of the treatment was obtained in 26 cases. All of the acutely presented dogs (6 cases) were cured and recurrence was the feature of the chronic cases. In addition, the presentation and management of ten cases referred to the Soft Tissue Surgery Unit are described